A Survey on the Current Status and Future Challenges Towards Objective Skills Assessment in Endovascular Surgery

Minimally-invasive endovascular interventions have evolved rapidly over the past decade, facilitated by breakthroughs in medical imaging and sensing, instrumentation and most recently robotics. Catheter based operations are potentially safer and applicable to a wider patient population due to the reduced comorbidity. As a result endovascular surgery has become the preferred treatment option for conditions previously treated with open surgery and as such the number of patients undergoing endovascular interventions is increasing every year. This fact coupled with a proclivity for reduced working hours, results in a requirement for efficient training and assessment of new surgeons, that deviates from the “see one, do one, teach one” model introduced by William Halsted, so that trainees obtain operational expertise in a shorter period. Developing more objective assessment tools based on quantitative metrics is now a recognised need in interventional training and this manuscript reports the current literature for endovascular skills assessment and the associated emerging technologies. A systematic search was performed on PubMed (MEDLINE), Google Scholar, IEEXplore and known journals using the keywords, “endovascular surgery”, “surgical skills”, “endovascular skills”, “surgical training endovascular” and “catheter skills”. Focusing explicitly on endovascular surgical skills, we group related works into three categories based on the metrics used; structured scales and checklists, simulation-based and motion-based metrics. This review highlights the key findings in each category and also provides suggestions for new research opportunities towards fully objective and automated surgical assessment solutions

Context-aware learning for robot-assisted endovascular catheterization

Endovascular intervention has become a mainstream treatment of cardiovascular diseases. However, multiple challenges remain such as unwanted radiation exposures, limited two-dimensional image guidance, insufficient force perception and haptic cues. Fast evolving robot-assisted platforms improve the stability and accuracy of instrument manipulation. The master-slave system also removes radiation to the operator. However, the integration of robotic systems into the current surgical workflow is still debatable since repetitive, easy tasks have little value to be executed by the robotic teleoperation. Current systems offer very low autonomy, potential autonomous features could bring more benefits such as reduced cognitive workloads and human error, safer and more consistent instrument manipulation, ability to incorporate various medical imaging and sensing modalities. This research proposes frameworks for automated catheterisation with different machine learning-based algorithms, includes Learning-from-Demonstration, Reinforcement Learning, and Imitation Learning. Those frameworks focused on integrating context for tasks in the process of skill learning, hence achieving better adaptation to different situations and safer tool-tissue interactions. Furthermore, the autonomous feature was applied to next-generation, MR-safe robotic catheterisation platform. The results provide important insights into improving catheter navigation in the form of autonomous task planning, self-optimization with clinical relevant factors, and motivate the design of intelligent, intuitive, and collaborative robots under non-ionizing image modalities.Open Acces

Haptic Interface for the Simulation of Endovascular Interventions

Endovascular interventions are minimally invasive surgical procedures that are performed to diagnose and treat vascular diseases. These interventions use a combination of long and flexible instruments known as guidewire and catheter. A popular method of developing the skills required to manipulate the instruments successfully is through the use of virtual reality (VR) simulators. However, the interfaces of current VR simulators have several shortcomings due to limitations in the instrument tracking and haptic feedback systems design. A major challenge of developing physics-based training simulations of endovascular interventional procedures is to unobtrusively access the central, co-axial guidewire for tracking and haptics. This work sets out to explore the state of the art, to identify and develop novel solutions to this concentric occlusion problem, and to perform a validation of a proof of concept prototype. This multi port haptic interface prototype has been integrated with a 3-D virtual environment and features novel instrument tracking and haptic feedback actuation systems. The former involves the use of an optical sensor to detect guidewire movements through a clear catheter, whereas the latter utilises the placement of a customised electromagnetic actuator within the catheter hub. During the proof of concept validation process, both systems received positive reviews. Whilst the haptic interface prototype designed in this work has met the original objectives, there are still important aspects which need to be addressed to improve its content and face validity. With further development, the prototype has the potential to evolve and become a significant improvement over the haptic interfaces that exist today.Open Acces

Translation of Intravascular Optical Ultrasound Imaging

ances in the field of intravascular imaging have provided clinicians with power ful tools to aid in the assessment and treatment of vascular pathology. Optical Ultra sound (OpUS) is an emerging modality with the potential to offer significant bene fits over existing commercial technologies such as intravascular ultrasound (IVUS) or optical coherence tomography (OCT). With this paradigm ultrasound (US) is generated using pulsed or modulated light and received by a miniaturised fibre-optic hydrophone (FOH). The US generation is facilitated through the use of engineered optically-absorbing nanocomposite materials. To date pre-clinical benchtop stud ies of OpUS have shown significant promise however further study is needed to facilitate clinical translation. The overall aim of this PhD was to develop a pathway to clinical translation of OpUS, enabled by the development of a catheter-based device capable of high resolution vascular tissue imaging during an in-vivo setting. A forward-viewing OpUS imaging probe was developed using a 400 µm mul timode optical fibre, dip-coated in a multi-walled carbon nanotube-PDMS com posite, paired with a FOH comprising a 125 µm single mode fibre tipped with a Fabry-Perot cavity. With this high US pressures were generated (21.5 MPa at the transducer surface) and broad corresponding bandwidths were achieved (−6 dB of 39.8MHz). Using this probe, OpUS imaging was performed of an ex-vivo human coronary artery. The results demonstrated excellent correspondence, in the detec tion of calcification and lipid infiltration, with IVUS, OCT and histological analysis. A side-viewing OpUS imaging probe, employing a reflective 45 °angle at the dis tal fibre surface, was used to demonstrate rotational B-mode imaging of a vascular structure for the first time. This provided high-resolution imaging (54 µm axial resolution) with deep depth penetration (>10.5 mm). Finally the clinical utility of this technology was demonstrated during an in-vivo endovascular procedure. An OpUS imaging probe, incorporated into an interventional device, allowed guidance of in-situ fenestration of an endograft during a complex abdominal aortic aneurysm repair. Through this work the potential clinical utility of OpUS, to assess pathology and guide vascular intervention, has been demonstrated. These results pave the way for translation of this technology and a first in man study

Constrained Stochastic State Estimation of Deformable 1D Objects: Application to Single-view 3D Reconstruction of Catheters with Radio-opaque Markers

International audienceMinimally invasive fluoroscopy-based procedures are the gold standard for diagnosis and treatment of various pathologies of the cardiovascular system. This kind of procedures imply for the clinicians to infer the 3D shape of the device from 2D images, which is known to be an ill-posed 10 problem. In this paper we present a method to reconstruct the 3D shape of the interventional device, with the aim of improving the navigation. The method combines a physics-based simulation with non-linear Bayesian filter. Whereas the physics-based model provides a prediction of the shape of the device navigating within the blood vessels (taking into account non-linear interactions be-15 tween the catheter and the surrounding anatomy), an Unscented Kalman Filter is used to correct the navigation model using 2D image features as external observations. The proposed framework has been evaluated on both synthetic and real data, under different model parameterizations, filter parameters tuning and external observations data-sets. Comparing the reconstructed 3D shape with a known ground truth, for the synthetic data-set, we obtained average values for 3D Hausdorff Distance of $0.81 ± 0.53 mm$, for the 3D mean distance at the segment of $0.37 ± 0.17$ mm and an average 3D tip error of $0.24 ± 0.13 mm$. For the real data-set,we obtained an average 3D Hausdorff distance of $1.74 ± 0.77 mm$, a average 3D mean distance at the distal segment of 0.91 ± 0.14 mm, an average 3D error on the tip of $0.53 ± 0.09 mm$. These results show the ability of our method to retrieve the 3D shape of the device, under a variety of filter parameterizations and challenging conditions: uncertainties on model parameterization, ambiguous views and non-linear complex phenomena such as stick and slip motions

Autonomous robotic intracardiac catheter navigation using haptic vision

International audienceWhile all minimally invasive procedures involve navigating from a small incision in the skin to the site of the intervention, it has not been previously demonstrated how this can be done 10 autonomously. To show that autonomous navigation is possible, we investigated it in the hardest place to do it-inside the beating heart. We created a robotic catheter that can navigate through the blood-filled heart using wall-following algorithms inspired by positively thigmotactic animals. The catheter employs haptic vision, a hybrid sense using imaging for both touch-based surface identification and force sensing, to accomplish wall following inside the blood-filled heart. 15 Through in vivo animal experiments, we demonstrate that the performance of an autonomously-controlled robotic catheter rivals that of an experienced clinician. Autonomous navigation is a fundamental capability on which more sophisticated levels of autonomy can be built, e.g., to perform a procedure. Similar to the role of automation in fighter aircraft, such capabilities can free the clinician to focus on the most critical aspects of the procedure while providing precise and 20 repeatable tool motions independent of operator experience and fatigue

Modeling and Force Estimation of Cardiac Catheters for Haptics-enabled Tele-intervention

Robot-assisted cardiovascular intervention (RCI) systems have shown success in reducing the x-ray exposure to surgeons and patients during cardiovascular interventional procedures. RCI systems typically are teleoperated systems with leader-follower architecture. With such system architecture, the surgeon is placed out of the x-ray exposure zone and uses a console to control the robot remotely. Despite its success in reducing x-ray exposure, clinicians have identified the lack of force feedback as to its main technological limitation that can lead to vascular perforation of the patient’s vessels and even their death. The objective of this thesis was to develop, verify, and validate mechatronics technology for real-time accurate and robust haptic feedback rendering for RCI systems. To attain the thesis objective, first, a thorough review of the state-of-the-art clinical requirements, modeling approaches and methods, and current knowledge gaps for the provision of force feedback for RCI systems was performed. Afterward, a real-time tip force estimation method based on image-based shape-sensing and learning-from-simulation was developed and validated. The learning-based model was fairly accurate but required a large database for training which was computationally expensive. Next, a new mechanistic model, i.e., finite arc method (FAM) for soft robots was proposed, formulated, solved, and validated that allowed for fast and accurate modeling of catheter deformation. With FAM, the required training database for the proposed learning-from-simulation method would be generated with high speed and accuracy. In the end, to robustly relay the estimated forces from real-time imaging from the follower robot to the leader haptic device, a novel impedance-based force feedback rendering modality was proposed and implemented on a representative teleoperated RCI system for experimental validation. The proposed method was compared with the classical direct force reflection method and showed enhanced stability, robustness, and accuracy in the presence of communication disruption. The results of this thesis showed that the performance of the proposed integrated force feedback rendering system was in fair compliance with the clinical requirements and had superior robustness compared to the classical direct force reflection method

Surgical skills modeling in cardiac ablation using deep learning

Cardiovascular diseases, a leading global cause of death, can be treated using Minimally Invasive Surgery (MIS) for various heart conditions. Cardiac ablation is an example of MIS, treating heart rhythm disorders like atrial fibrillation and the operation outcomes are highly dependent on the surgeon's skills. This procedure utilizes catheters, flexible endovascular devices inserted into the patient's blood vessels through a small incision. Traditionally, novice surgeons' performance is assessed in the Operating Room (OR) through surgical tasks. Unskilled behavior can lead to longer operations and inferior surgical outcomes. However, an alternative approach can be capturing surgeons' maneuvers and using them as input for an AI model to evaluate their skills outside the OR. To this end, two experimental setups were proposed to study the skills modelling for surgical behaviours. The first setup simulates the ablation procedure using a mechanical system with a synthetic heartbeat mechanism that measures contact forces between the catheter's tip and tissue. The second one simulates the cardiac catheterization procedure for the surgeon’s practice and records the user's maneuvers at the same time. The first task involved maintaining the force within a safe range while the tip of the catheter is touching the surface. The second task was passing a catheter’s tip through curves and level-intersection on a transparent blood vessel phantom. To evaluate attendees' demonstrations, it is crucial to extract maneuver models for both expert and novice surgeons. Data from participants, including novices and experts, performing the task using the experimental setups, is compiled. Deep recurrent neural networks are employed to extract the model of skills by solving a binary classification problem, distinguishing between expert and novice maneuvers. The results demonstrate the proposed networks' ability to accurately distinguish between novice and expert surgical skills, achieving an accuracy of over 92%

Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons